How Accurate Is an Unmanned Aerial Vehicle Data-Based Model Applied on Satellite Imagery for Chlorophyll-a Estimation in Freshwater Bodies?

• Main Authors: Anas El-Alem, Karem Chokmani, Aarthi Venkatesan, Lhissou Rachid, Hachem Agili, Jean-Pierre Dedieu
• Format: Article
• Language: English
• Published: MDPI AG 2021-03-01
• Series: Remote Sensing
• Subjects: Sentinel-2; unmanned aerial vehicle; remote sensing; chlorophyll-a; machine learning; ensemble-based system
• Online Access: https://www.mdpi.com/2072-4292/13/6/1134

Optical sensors are increasingly sought to estimate the amount of chlorophyll a (chl_a) in freshwater bodies. Most, whether empirical or semi-empirical, are data-oriented. Two main limitations are often encountered in the development of such models. The availability of data needed for model calibration, validation, and testing and the locality of the model developed—the majority need a re-parameterization from lake to lake. An Unmanned aerial vehicle (UAV) data-based model for chl_a estimation is developed in this work and tested on Sentinel-2 imagery without any re-parametrization. The Ensemble-based system (EBS) algorithm was used to train the model. The leave-one-out cross validation technique was applied to evaluate the EBS, at a local scale, where results were satisfactory (R² = Nash = 0.94 and RMSE = 5.6 µg chl_a L⁻¹). A blind database (collected over 89 lakes) was used to challenge the EBS’ Sentine-2-derived chl_a estimates at a regional scale. Results were relatively less good, yet satisfactory (R² = 0.85, RMSE= 2.4 µg chl_a L⁻¹, and Nash = 0.79). However, the EBS has shown some failure to correctly retrieve chl_a concentration in highly turbid waterbodies. This particularity nonetheless does not affect EBS performance, since turbid waters can easily be pre-recognized and masked before the chl_a modeling.